Overview

Dataset statistics

Number of variables34
Number of observations28351
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory272.0 B

Variable types

Categorical21
Numeric13

Alerts

gender is highly imbalanced (50.0%)Imbalance
married is highly imbalanced (56.1%)Imbalance
familysize has 641 (2.3%) zerosZeros

Reproduction

Analysis started2024-02-19 11:30:39.262311
Analysis finished2024-02-19 11:31:46.696066
Duration1 minute and 7.43 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Q2A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
10362 
1
8277 
3
5117 
2
4595 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 10362
36.5%
1 8277
29.2%
3 5117
18.0%
2 4595
16.2%

Length

2024-02-19T11:31:46.860494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:47.120404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10362
36.5%
1 8277
29.2%
3 5117
18.0%
2 4595
16.2%

Most occurring characters

ValueCountFrequency (%)
0 10362
36.5%
1 8277
29.2%
3 5117
18.0%
2 4595
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10362
36.5%
1 8277
29.2%
3 5117
18.0%
2 4595
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10362
36.5%
1 8277
29.2%
3 5117
18.0%
2 4595
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10362
36.5%
1 8277
29.2%
3 5117
18.0%
2 4595
16.2%

Q4A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
13398 
1
7936 
2
3993 
3
3024 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row2
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 13398
47.3%
1 7936
28.0%
2 3993
 
14.1%
3 3024
 
10.7%

Length

2024-02-19T11:31:47.381977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:47.658720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13398
47.3%
1 7936
28.0%
2 3993
 
14.1%
3 3024
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 13398
47.3%
1 7936
28.0%
2 3993
 
14.1%
3 3024
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13398
47.3%
1 7936
28.0%
2 3993
 
14.1%
3 3024
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13398
47.3%
1 7936
28.0%
2 3993
 
14.1%
3 3024
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13398
47.3%
1 7936
28.0%
2 3993
 
14.1%
3 3024
 
10.7%

Q7A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
13703 
1
8102 
2
3721 
3
2825 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row1
5th row3

Common Values

ValueCountFrequency (%)
0 13703
48.3%
1 8102
28.6%
2 3721
 
13.1%
3 2825
 
10.0%

Length

2024-02-19T11:31:47.910979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:48.178508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13703
48.3%
1 8102
28.6%
2 3721
 
13.1%
3 2825
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 13703
48.3%
1 8102
28.6%
2 3721
 
13.1%
3 2825
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13703
48.3%
1 8102
28.6%
2 3721
 
13.1%
3 2825
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13703
48.3%
1 8102
28.6%
2 3721
 
13.1%
3 2825
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13703
48.3%
1 8102
28.6%
2 3721
 
13.1%
3 2825
 
10.0%

Q9A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
1
8899 
3
7772 
2
6858 
0
4822 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row0
5th row3

Common Values

ValueCountFrequency (%)
1 8899
31.4%
3 7772
27.4%
2 6858
24.2%
0 4822
17.0%

Length

2024-02-19T11:31:48.448493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:48.709112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 8899
31.4%
3 7772
27.4%
2 6858
24.2%
0 4822
17.0%

Most occurring characters

ValueCountFrequency (%)
1 8899
31.4%
3 7772
27.4%
2 6858
24.2%
0 4822
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8899
31.4%
3 7772
27.4%
2 6858
24.2%
0 4822
17.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8899
31.4%
3 7772
27.4%
2 6858
24.2%
0 4822
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8899
31.4%
3 7772
27.4%
2 6858
24.2%
0 4822
17.0%

Q15A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
14721 
1
7932 
2
3415 
3
2283 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row1
5th row3

Common Values

ValueCountFrequency (%)
0 14721
51.9%
1 7932
28.0%
2 3415
 
12.0%
3 2283
 
8.1%

Length

2024-02-19T11:31:48.952735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:49.221787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14721
51.9%
1 7932
28.0%
2 3415
 
12.0%
3 2283
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 14721
51.9%
1 7932
28.0%
2 3415
 
12.0%
3 2283
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14721
51.9%
1 7932
28.0%
2 3415
 
12.0%
3 2283
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14721
51.9%
1 7932
28.0%
2 3415
 
12.0%
3 2283
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14721
51.9%
1 7932
28.0%
2 3415
 
12.0%
3 2283
 
8.1%

Q19A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
13978 
1
7328 
2
3615 
3
3430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row0
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 13978
49.3%
1 7328
25.8%
2 3615
 
12.8%
3 3430
 
12.1%

Length

2024-02-19T11:31:49.498239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:49.766768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13978
49.3%
1 7328
25.8%
2 3615
 
12.8%
3 3430
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 13978
49.3%
1 7328
25.8%
2 3615
 
12.8%
3 3430
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13978
49.3%
1 7328
25.8%
2 3615
 
12.8%
3 3430
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13978
49.3%
1 7328
25.8%
2 3615
 
12.8%
3 3430
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13978
49.3%
1 7328
25.8%
2 3615
 
12.8%
3 3430
 
12.1%

Q20A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
8882 
1
8424 
3
5626 
2
5419 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row1
5th row3

Common Values

ValueCountFrequency (%)
0 8882
31.3%
1 8424
29.7%
3 5626
19.8%
2 5419
19.1%

Length

2024-02-19T11:31:50.017381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:50.281028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8882
31.3%
1 8424
29.7%
3 5626
19.8%
2 5419
19.1%

Most occurring characters

ValueCountFrequency (%)
0 8882
31.3%
1 8424
29.7%
3 5626
19.8%
2 5419
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8882
31.3%
1 8424
29.7%
3 5626
19.8%
2 5419
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8882
31.3%
1 8424
29.7%
3 5626
19.8%
2 5419
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8882
31.3%
1 8424
29.7%
3 5626
19.8%
2 5419
19.1%

Q23A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
18481 
1
6337 
2
2194 
3
 
1339

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row2
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 18481
65.2%
1 6337
 
22.4%
2 2194
 
7.7%
3 1339
 
4.7%

Length

2024-02-19T11:31:50.545082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:50.842324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18481
65.2%
1 6337
 
22.4%
2 2194
 
7.7%
3 1339
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 18481
65.2%
1 6337
 
22.4%
2 2194
 
7.7%
3 1339
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18481
65.2%
1 6337
 
22.4%
2 2194
 
7.7%
3 1339
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18481
65.2%
1 6337
 
22.4%
2 2194
 
7.7%
3 1339
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18481
65.2%
1 6337
 
22.4%
2 2194
 
7.7%
3 1339
 
4.7%

Q25A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
10080 
1
8708 
2
5242 
3
4321 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 10080
35.6%
1 8708
30.7%
2 5242
18.5%
3 4321
15.2%

Length

2024-02-19T11:31:51.091462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:51.351850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10080
35.6%
1 8708
30.7%
2 5242
18.5%
3 4321
15.2%

Most occurring characters

ValueCountFrequency (%)
0 10080
35.6%
1 8708
30.7%
2 5242
18.5%
3 4321
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10080
35.6%
1 8708
30.7%
2 5242
18.5%
3 4321
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10080
35.6%
1 8708
30.7%
2 5242
18.5%
3 4321
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10080
35.6%
1 8708
30.7%
2 5242
18.5%
3 4321
15.2%

Q28A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
9672 
1
9238 
2
5196 
3
4245 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row1
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 9672
34.1%
1 9238
32.6%
2 5196
18.3%
3 4245
15.0%

Length

2024-02-19T11:31:51.612395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:51.900495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9672
34.1%
1 9238
32.6%
2 5196
18.3%
3 4245
15.0%

Most occurring characters

ValueCountFrequency (%)
0 9672
34.1%
1 9238
32.6%
2 5196
18.3%
3 4245
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9672
34.1%
1 9238
32.6%
2 5196
18.3%
3 4245
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9672
34.1%
1 9238
32.6%
2 5196
18.3%
3 4245
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9672
34.1%
1 9238
32.6%
2 5196
18.3%
3 4245
15.0%

Q30A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
1
9264 
0
7224 
2
6177 
3
5686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 9264
32.7%
0 7224
25.5%
2 6177
21.8%
3 5686
20.1%

Length

2024-02-19T11:31:52.157153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:52.419538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9264
32.7%
0 7224
25.5%
2 6177
21.8%
3 5686
20.1%

Most occurring characters

ValueCountFrequency (%)
1 9264
32.7%
0 7224
25.5%
2 6177
21.8%
3 5686
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9264
32.7%
0 7224
25.5%
2 6177
21.8%
3 5686
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9264
32.7%
0 7224
25.5%
2 6177
21.8%
3 5686
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9264
32.7%
0 7224
25.5%
2 6177
21.8%
3 5686
20.1%

Q36A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
8990 
1
8815 
3
5375 
2
5171 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row1
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 8990
31.7%
1 8815
31.1%
3 5375
19.0%
2 5171
18.2%

Length

2024-02-19T11:31:52.675828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:52.953606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8990
31.7%
1 8815
31.1%
3 5375
19.0%
2 5171
18.2%

Most occurring characters

ValueCountFrequency (%)
0 8990
31.7%
1 8815
31.1%
3 5375
19.0%
2 5171
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8990
31.7%
1 8815
31.1%
3 5375
19.0%
2 5171
18.2%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8990
31.7%
1 8815
31.1%
3 5375
19.0%
2 5171
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8990
31.7%
1 8815
31.1%
3 5375
19.0%
2 5171
18.2%

Q40A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
1
8184 
3
7984 
2
6397 
0
5786 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row0
5th row3

Common Values

ValueCountFrequency (%)
1 8184
28.9%
3 7984
28.2%
2 6397
22.6%
0 5786
20.4%

Length

2024-02-19T11:31:53.198182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:53.466987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 8184
28.9%
3 7984
28.2%
2 6397
22.6%
0 5786
20.4%

Most occurring characters

ValueCountFrequency (%)
1 8184
28.9%
3 7984
28.2%
2 6397
22.6%
0 5786
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8184
28.9%
3 7984
28.2%
2 6397
22.6%
0 5786
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8184
28.9%
3 7984
28.2%
2 6397
22.6%
0 5786
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8184
28.9%
3 7984
28.2%
2 6397
22.6%
0 5786
20.4%

Q41A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
0
13103 
1
8366 
2
3782 
3
3100 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 13103
46.2%
1 8366
29.5%
2 3782
 
13.3%
3 3100
 
10.9%

Length

2024-02-19T11:31:53.726228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:31:53.992748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13103
46.2%
1 8366
29.5%
2 3782
 
13.3%
3 3100
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 13103
46.2%
1 8366
29.5%
2 3782
 
13.3%
3 3100
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13103
46.2%
1 8366
29.5%
2 3782
 
13.3%
3 3100
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13103
46.2%
1 8366
29.5%
2 3782
 
13.3%
3 3100
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13103
46.2%
1 8366
29.5%
2 3782
 
13.3%
3 3100
 
10.9%

TIPI1
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.992099
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:54.198158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8175145
Coefficient of variation (CV)0.45527792
Kurtosis-1.0716519
Mean3.992099
Median Absolute Deviation (MAD)1
Skewness-0.22059051
Sum113180
Variance3.3033591
MonotonicityNot monotonic
2024-02-19T11:31:54.407132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 6673
23.5%
6 4667
16.5%
4 4503
15.9%
1 3767
13.3%
2 3609
12.7%
3 3277
11.6%
7 1855
 
6.5%
ValueCountFrequency (%)
1 3767
13.3%
2 3609
12.7%
3 3277
11.6%
4 4503
15.9%
5 6673
23.5%
6 4667
16.5%
7 1855
 
6.5%
ValueCountFrequency (%)
7 1855
 
6.5%
6 4667
16.5%
5 6673
23.5%
4 4503
15.9%
3 3277
11.6%
2 3609
12.7%
1 3767
13.3%

TIPI2
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2412261
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:54.659946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.759141
Coefficient of variation (CV)0.4147718
Kurtosis-0.84088102
Mean4.2412261
Median Absolute Deviation (MAD)1
Skewness-0.39233968
Sum120243
Variance3.0945769
MonotonicityNot monotonic
2024-02-19T11:31:54.986174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 7790
27.5%
6 5006
17.7%
4 4327
15.3%
3 3100
 
10.9%
2 2937
 
10.4%
1 2927
 
10.3%
7 2264
 
8.0%
ValueCountFrequency (%)
1 2927
 
10.3%
2 2937
 
10.4%
3 3100
 
10.9%
4 4327
15.3%
5 7790
27.5%
6 5006
17.7%
7 2264
 
8.0%
ValueCountFrequency (%)
7 2264
 
8.0%
6 5006
17.7%
5 7790
27.5%
4 4327
15.3%
3 3100
 
10.9%
2 2937
 
10.4%
1 2927
 
10.3%

TIPI3
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9452224
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:55.409865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6651202
Coefficient of variation (CV)0.33671291
Kurtosis-0.2360681
Mean4.9452224
Median Absolute Deviation (MAD)1
Skewness-0.77662305
Sum140202
Variance2.7726254
MonotonicityNot monotonic
2024-02-19T11:31:55.808172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 8186
28.9%
5 6609
23.3%
7 4712
16.6%
4 3078
 
10.9%
3 2587
 
9.1%
2 1805
 
6.4%
1 1374
 
4.8%
ValueCountFrequency (%)
1 1374
 
4.8%
2 1805
 
6.4%
3 2587
 
9.1%
4 3078
 
10.9%
5 6609
23.3%
6 8186
28.9%
7 4712
16.6%
ValueCountFrequency (%)
7 4712
16.6%
6 8186
28.9%
5 6609
23.3%
4 3078
 
10.9%
3 2587
 
9.1%
2 1805
 
6.4%
1 1374
 
4.8%

TIPI4
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1615463
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:56.172838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7680899
Coefficient of variation (CV)0.34255042
Kurtosis-0.13184606
Mean5.1615463
Median Absolute Deviation (MAD)1
Skewness-0.90425113
Sum146335
Variance3.1261417
MonotonicityNot monotonic
2024-02-19T11:31:56.533939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 7822
27.6%
6 6637
23.4%
5 6485
22.9%
4 2114
 
7.5%
3 1867
 
6.6%
2 1851
 
6.5%
1 1575
 
5.6%
ValueCountFrequency (%)
1 1575
 
5.6%
2 1851
 
6.5%
3 1867
 
6.6%
4 2114
 
7.5%
5 6485
22.9%
6 6637
23.4%
7 7822
27.6%
ValueCountFrequency (%)
7 7822
27.6%
6 6637
23.4%
5 6485
22.9%
4 2114
 
7.5%
3 1867
 
6.6%
2 1851
 
6.5%
1 1575
 
5.6%

TIPI5
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1309654
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:56.977893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5748545
Coefficient of variation (CV)0.30693142
Kurtosis0.065652378
Mean5.1309654
Median Absolute Deviation (MAD)1
Skewness-0.8248065
Sum145468
Variance2.4801667
MonotonicityNot monotonic
2024-02-19T11:31:57.405395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 7680
27.1%
5 6756
23.8%
7 5883
20.8%
4 3602
12.7%
3 2114
 
7.5%
2 1361
 
4.8%
1 955
 
3.4%
ValueCountFrequency (%)
1 955
 
3.4%
2 1361
 
4.8%
3 2114
 
7.5%
4 3602
12.7%
5 6756
23.8%
6 7680
27.1%
7 5883
20.8%
ValueCountFrequency (%)
7 5883
20.8%
6 7680
27.1%
5 6756
23.8%
4 3602
12.7%
3 2114
 
7.5%
2 1361
 
4.8%
1 955
 
3.4%

TIPI6
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8525272
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:57.873786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8543282
Coefficient of variation (CV)0.38213659
Kurtosis-0.69268124
Mean4.8525272
Median Absolute Deviation (MAD)1
Skewness-0.6022411
Sum137574
Variance3.4385332
MonotonicityNot monotonic
2024-02-19T11:31:58.248586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 6729
23.7%
6 5645
19.9%
5 5606
19.8%
4 3635
12.8%
3 2596
 
9.2%
2 2103
 
7.4%
1 2037
 
7.2%
ValueCountFrequency (%)
1 2037
 
7.2%
2 2103
 
7.4%
3 2596
 
9.2%
4 3635
12.8%
5 5606
19.8%
6 5645
19.9%
7 6729
23.7%
ValueCountFrequency (%)
7 6729
23.7%
6 5645
19.9%
5 5606
19.8%
4 3635
12.8%
3 2596
 
9.2%
2 2103
 
7.4%
1 2037
 
7.2%

TIPI7
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.442771
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:58.596446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4277533
Coefficient of variation (CV)0.26232104
Kurtosis0.77229281
Mean5.442771
Median Absolute Deviation (MAD)1
Skewness-1.028738
Sum154308
Variance2.0384796
MonotonicityNot monotonic
2024-02-19T11:31:58.836342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 8586
30.3%
7 7349
25.9%
5 6496
22.9%
4 3058
 
10.8%
3 1471
 
5.2%
2 833
 
2.9%
1 558
 
2.0%
ValueCountFrequency (%)
1 558
 
2.0%
2 833
 
2.9%
3 1471
 
5.2%
4 3058
 
10.8%
5 6496
22.9%
6 8586
30.3%
7 7349
25.9%
ValueCountFrequency (%)
7 7349
25.9%
6 8586
30.3%
5 6496
22.9%
4 3058
 
10.8%
3 1471
 
5.2%
2 833
 
2.9%
1 558
 
2.0%

TIPI8
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2877853
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:59.049554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8837555
Coefficient of variation (CV)0.43933065
Kurtosis-1.0347549
Mean4.2877853
Median Absolute Deviation (MAD)1
Skewness-0.2924899
Sum121563
Variance3.5485347
MonotonicityNot monotonic
2024-02-19T11:31:59.268892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 7134
25.2%
6 4410
15.6%
7 3782
13.3%
2 3351
11.8%
4 3333
11.8%
3 3292
11.6%
1 3049
10.8%
ValueCountFrequency (%)
1 3049
10.8%
2 3351
11.8%
3 3292
11.6%
4 3333
11.8%
5 7134
25.2%
6 4410
15.6%
7 3782
13.3%
ValueCountFrequency (%)
7 3782
13.3%
6 4410
15.6%
5 7134
25.2%
4 3333
11.8%
3 3292
11.6%
2 3351
11.8%
1 3049
10.8%

TIPI9
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8634263
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:59.519600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7627058
Coefficient of variation (CV)0.45625454
Kurtosis-0.98635274
Mean3.8634263
Median Absolute Deviation (MAD)1
Skewness0.016535992
Sum109532
Variance3.1071318
MonotonicityNot monotonic
2024-02-19T11:31:59.744866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 5433
19.2%
5 4864
17.2%
3 4828
17.0%
2 4088
14.4%
6 3934
13.9%
1 3202
11.3%
7 2002
 
7.1%
ValueCountFrequency (%)
1 3202
11.3%
2 4088
14.4%
3 4828
17.0%
4 5433
19.2%
5 4864
17.2%
6 3934
13.9%
7 2002
 
7.1%
ValueCountFrequency (%)
7 2002
 
7.1%
6 3934
13.9%
5 4864
17.2%
4 5433
19.2%
3 4828
17.0%
2 4088
14.4%
1 3202
11.3%

TIPI10
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8478361
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:31:59.996363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7893233
Coefficient of variation (CV)0.46502066
Kurtosis-0.93414986
Mean3.8478361
Median Absolute Deviation (MAD)1
Skewness0.04054492
Sum109090
Variance3.2016778
MonotonicityNot monotonic
2024-02-19T11:32:00.206444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 6025
21.3%
5 4959
17.5%
3 4381
15.5%
2 3904
13.8%
1 3546
12.5%
6 3054
10.8%
7 2482
8.8%
ValueCountFrequency (%)
1 3546
12.5%
2 3904
13.8%
3 4381
15.5%
4 6025
21.3%
5 4959
17.5%
6 3054
10.8%
7 2482
8.8%
ValueCountFrequency (%)
7 2482
8.8%
6 3054
10.8%
5 4959
17.5%
4 6025
21.3%
3 4381
15.5%
2 3904
13.8%
1 3546
12.5%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
3
14890 
2
8037 
4
4702 
1
 
722

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
3 14890
52.5%
2 8037
28.3%
4 4702
 
16.6%
1 722
 
2.5%

Length

2024-02-19T11:32:00.461678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:32:00.734331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 14890
52.5%
2 8037
28.3%
4 4702
 
16.6%
1 722
 
2.5%

Most occurring characters

ValueCountFrequency (%)
3 14890
52.5%
2 8037
28.3%
4 4702
 
16.6%
1 722
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14890
52.5%
2 8037
28.3%
4 4702
 
16.6%
1 722
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 14890
52.5%
2 8037
28.3%
4 4702
 
16.6%
1 722
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 14890
52.5%
2 8037
28.3%
4 4702
 
16.6%
1 722
 
2.5%

urban
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
3
12870 
2
9277 
1
6204 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 12870
45.4%
2 9277
32.7%
1 6204
21.9%

Length

2024-02-19T11:32:00.997719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:32:01.266431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 12870
45.4%
2 9277
32.7%
1 6204
21.9%

Most occurring characters

ValueCountFrequency (%)
3 12870
45.4%
2 9277
32.7%
1 6204
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12870
45.4%
2 9277
32.7%
1 6204
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 12870
45.4%
2 9277
32.7%
1 6204
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 12870
45.4%
2 9277
32.7%
1 6204
21.9%

gender
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
2
22329 
1
5815 
3
 
207

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 22329
78.8%
1 5815
 
20.5%
3 207
 
0.7%

Length

2024-02-19T11:32:01.487180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:32:01.742080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 22329
78.8%
1 5815
 
20.5%
3 207
 
0.7%

Most occurring characters

ValueCountFrequency (%)
2 22329
78.8%
1 5815
 
20.5%
3 207
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22329
78.8%
1 5815
 
20.5%
3 207
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 22329
78.8%
1 5815
 
20.5%
3 207
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 22329
78.8%
1 5815
 
20.5%
3 207
 
0.7%

religion
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2444358
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:32:01.981683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median10
Q310
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.141945
Coefficient of variation (CV)0.38109885
Kurtosis0.12552317
Mean8.2444358
Median Absolute Deviation (MAD)0
Skewness-1.2891632
Sum233738
Variance9.8718187
MonotonicityNot monotonic
2024-02-19T11:32:02.243540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 19009
67.0%
2 1806
 
6.4%
4 1795
 
6.3%
1 1685
 
5.9%
7 1087
 
3.8%
6 1009
 
3.6%
12 890
 
3.1%
8 546
 
1.9%
3 358
 
1.3%
9 73
 
0.3%
Other values (2) 93
 
0.3%
ValueCountFrequency (%)
1 1685
 
5.9%
2 1806
 
6.4%
3 358
 
1.3%
4 1795
 
6.3%
5 49
 
0.2%
6 1009
 
3.6%
7 1087
 
3.8%
8 546
 
1.9%
9 73
 
0.3%
10 19009
67.0%
ValueCountFrequency (%)
12 890
 
3.1%
11 44
 
0.2%
10 19009
67.0%
9 73
 
0.3%
8 546
 
1.9%
7 1087
 
3.8%
6 1009
 
3.6%
5 49
 
0.2%
4 1795
 
6.3%
3 358
 
1.3%

orientation
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
1
20264 
2
2830 
5
2501 
4
 
1420
3
 
1336

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 20264
71.5%
2 2830
 
10.0%
5 2501
 
8.8%
4 1420
 
5.0%
3 1336
 
4.7%

Length

2024-02-19T11:32:02.500488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:32:02.762549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 20264
71.5%
2 2830
 
10.0%
5 2501
 
8.8%
4 1420
 
5.0%
3 1336
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 20264
71.5%
2 2830
 
10.0%
5 2501
 
8.8%
4 1420
 
5.0%
3 1336
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20264
71.5%
2 2830
 
10.0%
5 2501
 
8.8%
4 1420
 
5.0%
3 1336
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20264
71.5%
2 2830
 
10.0%
5 2501
 
8.8%
4 1420
 
5.0%
3 1336
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20264
71.5%
2 2830
 
10.0%
5 2501
 
8.8%
4 1420
 
5.0%
3 1336
 
4.7%

race
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.685478
Minimum10
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:32:02.987798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median10
Q360
95-th percentile70
Maximum70
Range60
Interquartile range (IQR)50

Descriptive statistics

Standard deviation24.833438
Coefficient of variation (CV)0.93059743
Kurtosis-1.1610697
Mean26.685478
Median Absolute Deviation (MAD)0
Skewness0.87184842
Sum756560
Variance616.69963
MonotonicityNot monotonic
2024-02-19T11:32:03.199515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 19176
67.6%
60 5125
 
18.1%
70 3410
 
12.0%
30 320
 
1.1%
20 230
 
0.8%
50 80
 
0.3%
40 10
 
< 0.1%
ValueCountFrequency (%)
10 19176
67.6%
20 230
 
0.8%
30 320
 
1.1%
40 10
 
< 0.1%
50 80
 
0.3%
60 5125
 
18.1%
70 3410
 
12.0%
ValueCountFrequency (%)
70 3410
 
12.0%
60 5125
 
18.1%
50 80
 
0.3%
40 10
 
< 0.1%
30 320
 
1.1%
20 230
 
0.8%
10 19176
67.6%

married
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
1
24216 
2
3420 
3
 
715

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 24216
85.4%
2 3420
 
12.1%
3 715
 
2.5%

Length

2024-02-19T11:32:03.451005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:32:03.708378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 24216
85.4%
2 3420
 
12.1%
3 715
 
2.5%

Most occurring characters

ValueCountFrequency (%)
1 24216
85.4%
2 3420
 
12.1%
3 715
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24216
85.4%
2 3420
 
12.1%
3 715
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24216
85.4%
2 3420
 
12.1%
3 715
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24216
85.4%
2 3420
 
12.1%
3 715
 
2.5%

familysize
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7123206
Minimum0
Maximum15
Zeros641
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2024-02-19T11:32:03.920665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9267877
Coefficient of variation (CV)0.51902515
Kurtosis1.7293053
Mean3.7123206
Median Absolute Deviation (MAD)1
Skewness0.91817714
Sum105248
Variance3.712511
MonotonicityNot monotonic
2024-02-19T11:32:04.168314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 6401
22.6%
4 5857
20.7%
2 5532
19.5%
5 3925
13.8%
6 2046
 
7.2%
1 1728
 
6.1%
7 1055
 
3.7%
0 641
 
2.3%
8 558
 
2.0%
9 279
 
1.0%
Other values (6) 329
 
1.2%
ValueCountFrequency (%)
0 641
 
2.3%
1 1728
 
6.1%
2 5532
19.5%
3 6401
22.6%
4 5857
20.7%
5 3925
13.8%
6 2046
 
7.2%
7 1055
 
3.7%
8 558
 
2.0%
9 279
 
1.0%
ValueCountFrequency (%)
15 4
 
< 0.1%
14 8
 
< 0.1%
13 15
 
0.1%
12 40
 
0.1%
11 97
 
0.3%
10 165
 
0.6%
9 279
 
1.0%
8 558
 
2.0%
7 1055
3.7%
6 2046
7.2%

age_group
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
4
13373 
3
11595 
5
1514 
2
 
1106
6
 
763

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28351
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row4
5th row2

Common Values

ValueCountFrequency (%)
4 13373
47.2%
3 11595
40.9%
5 1514
 
5.3%
2 1106
 
3.9%
6 763
 
2.7%

Length

2024-02-19T11:32:05.423529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:32:05.697803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 13373
47.2%
3 11595
40.9%
5 1514
 
5.3%
2 1106
 
3.9%
6 763
 
2.7%

Most occurring characters

ValueCountFrequency (%)
4 13373
47.2%
3 11595
40.9%
5 1514
 
5.3%
2 1106
 
3.9%
6 763
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 13373
47.2%
3 11595
40.9%
5 1514
 
5.3%
2 1106
 
3.9%
6 763
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 28351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 13373
47.2%
3 11595
40.9%
5 1514
 
5.3%
2 1106
 
3.9%
6 763
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 13373
47.2%
3 11595
40.9%
5 1514
 
5.3%
2 1106
 
3.9%
6 763
 
2.7%

predict
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.6 KiB
Extremely Severe
9283 
Normal
7503 
Moderate
5159 
Severe
4325 
Mild
2081 

Length

Max length16
Median length8
Mean length9.4914465
Min length4

Characters and Unicode

Total characters269092
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSevere
2nd rowExtremely Severe
3rd rowSevere
4th rowNormal
5th rowExtremely Severe

Common Values

ValueCountFrequency (%)
Extremely Severe 9283
32.7%
Normal 7503
26.5%
Moderate 5159
18.2%
Severe 4325
15.3%
Mild 2081
 
7.3%

Length

2024-02-19T11:32:05.967589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:32:06.273156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
severe 13608
36.2%
extremely 9283
24.7%
normal 7503
19.9%
moderate 5159
 
13.7%
mild 2081
 
5.5%

Most occurring characters

ValueCountFrequency (%)
e 69708
25.9%
r 35553
13.2%
l 18867
 
7.0%
m 16786
 
6.2%
t 14442
 
5.4%
v 13608
 
5.1%
S 13608
 
5.1%
a 12662
 
4.7%
o 12662
 
4.7%
E 9283
 
3.4%
Other values (7) 51913
19.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 222175
82.6%
Uppercase Letter 37634
 
14.0%
Space Separator 9283
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 69708
31.4%
r 35553
16.0%
l 18867
 
8.5%
m 16786
 
7.6%
t 14442
 
6.5%
v 13608
 
6.1%
a 12662
 
5.7%
o 12662
 
5.7%
x 9283
 
4.2%
y 9283
 
4.2%
Other values (2) 9321
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
S 13608
36.2%
E 9283
24.7%
N 7503
19.9%
M 7240
19.2%
Space Separator
ValueCountFrequency (%)
9283
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 259809
96.6%
Common 9283
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 69708
26.8%
r 35553
13.7%
l 18867
 
7.3%
m 16786
 
6.5%
t 14442
 
5.6%
v 13608
 
5.2%
S 13608
 
5.2%
a 12662
 
4.9%
o 12662
 
4.9%
E 9283
 
3.6%
Other values (6) 42630
16.4%
Common
ValueCountFrequency (%)
9283
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269092
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 69708
25.9%
r 35553
13.2%
l 18867
 
7.0%
m 16786
 
6.2%
t 14442
 
5.4%
v 13608
 
5.1%
S 13608
 
5.1%
a 12662
 
4.7%
o 12662
 
4.7%
E 9283
 
3.4%
Other values (7) 51913
19.3%

Interactions

2024-02-19T11:31:40.329940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:48.915842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:52.425506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:55.832275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:59.696776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:04.279929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:11.578710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:17.441705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:20.908646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:24.366679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:28.047113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:32.445668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:36.834282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:40.592457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:49.227017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:52.684797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:56.099454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:00.118230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:06.147306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:12.180035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:17.706513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:21.159350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:24.628059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:28.418278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:32.710777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:37.104097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:40.896942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:49.508379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:52.939053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:56.380853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:00.523496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:06.400110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:13.190071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:17.969217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:21.433854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:24.889416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:28.813195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:32.984504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:37.374360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:41.328871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:49.782660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:53.209388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:56.654732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:00.890595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:06.668165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:13.887216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:18.243891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:21.698520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:25.147704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:29.174243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:33.242816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:37.635844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:41.656882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:50.052959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:53.464110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:56.916352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:01.264464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:06.927343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:14.298432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:18.501185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:21.963625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:25.410785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:29.625321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:33.519953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:37.911226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:42.006529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:50.326006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:53.718538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:57.194120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:01.677262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:07.223586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:14.668578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:18.771018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:22.245442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:25.667486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:30.066100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:34.629774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:38.175950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:42.378395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:50.590544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:53.972628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:57.475703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:02.096633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:07.723552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:14.954359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:19.044885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:22.502011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:25.917321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:30.461148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:34.903839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:38.448528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:42.793324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:50.850633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:54.235306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:57.742313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:02.544132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:08.257864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:15.348588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:19.315246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:22.771600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:26.174424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:30.844151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:35.210990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:38.707752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:43.181391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:51.114141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:54.495198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:58.022376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:02.957066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:08.751716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:15.749068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:19.593848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:23.034045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:26.448667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:31.108601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:35.485944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:38.984915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:43.584363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:51.372390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:54.743762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:58.330225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:03.226932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:09.528849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:16.159895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:19.858909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:23.300114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:26.717939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:31.369149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:35.765914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:39.252460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:43.968045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:51.630488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:54.998852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:58.609725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:03.501937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:10.008429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:16.567035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:20.123324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:23.560219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:27.078999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:31.628873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:36.037782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:39.516306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:44.348189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:51.889458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:55.259789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:58.879718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:03.763010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:10.552951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:16.914582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:20.380666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:23.826409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:27.412771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:31.904269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:36.308900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:39.775812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:44.750582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:52.151477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:55.546459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:59.292124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:04.021083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:11.106809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:17.178140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:20.645288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:24.094558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:27.730951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:32.173532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:36.573942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:31:40.056085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-19T11:32:06.559634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Q15AQ19AQ20AQ23AQ25AQ28AQ2AQ30AQ36AQ40AQ41AQ4AQ7AQ9ATIPI1TIPI10TIPI2TIPI3TIPI4TIPI5TIPI6TIPI7TIPI8TIPI9age_groupeducationfamilysizegendermarriedorientationpredictracereligionurban
Q15A1.0000.2190.2610.2900.2730.2860.2120.2330.2730.2320.2730.3090.3140.223-0.0730.0520.111-0.0810.296-0.1000.1040.0110.128-0.2420.0750.0460.0110.0600.0540.0520.359-0.0210.0370.030
Q19A0.2191.0000.2230.2220.2480.2420.2060.2110.2150.2230.2850.2530.2830.223-0.0560.0720.102-0.0800.285-0.1170.0810.0070.134-0.2100.1080.0770.0020.0240.0860.0470.338-0.0400.0580.027
Q20A0.2610.2231.0000.2400.2730.3690.2080.3240.4170.3470.2660.2760.2880.329-0.1100.1150.121-0.1320.446-0.1790.1440.0180.186-0.3440.1110.0630.0240.1050.1150.0510.454-0.0710.0880.032
Q23A0.2900.2220.2401.0000.2440.2500.2310.2110.2510.2070.2630.2960.2880.200-0.0570.0720.107-0.0790.272-0.1070.094-0.0110.133-0.2090.0810.0490.0440.0610.0610.0550.332-0.0490.0800.037
Q25A0.2730.2480.2730.2441.0000.3080.2240.2370.2620.2530.2970.3680.3080.263-0.0690.0390.110-0.0640.330-0.0920.1040.0350.122-0.2540.0680.046-0.0040.0490.0620.0490.399-0.0170.0340.019
Q28A0.2860.2420.3690.2500.3081.0000.2060.2940.3740.3580.3010.3210.3220.344-0.1030.0700.129-0.1130.446-0.1510.1240.0340.169-0.3520.0900.063-0.0230.0830.0770.0510.463-0.0060.0240.026
Q2A0.2120.2060.2080.2310.2240.2061.0000.2040.2150.2100.2200.2260.2300.206-0.0520.0840.111-0.0710.248-0.0780.0870.0080.137-0.1880.0730.0410.0590.0390.0690.0310.321-0.0890.1050.024
Q30A0.2330.2110.3240.2110.2370.2940.2041.0000.3330.3480.2340.2260.2450.327-0.1330.1410.128-0.1490.393-0.1850.1570.0100.207-0.3080.1120.0620.0430.0730.1150.0380.407-0.0870.1010.019
Q36A0.2730.2150.4170.2510.2620.3740.2150.3331.0000.3400.2720.2760.2860.337-0.0920.1320.137-0.1460.440-0.1740.1380.0240.203-0.3510.1220.0620.0670.1010.1150.0440.458-0.1200.1450.035
Q40A0.2320.2230.3470.2070.2530.3580.2100.3480.3401.0000.2550.2540.2630.369-0.1330.1360.129-0.1360.451-0.1830.1650.0330.204-0.3340.1540.0870.0310.0850.1470.0400.443-0.0970.1040.029
Q41A0.2730.2850.2660.2630.2970.3010.2200.2340.2720.2551.0000.3170.4340.255-0.0750.0610.108-0.0820.322-0.1190.1010.0120.143-0.2540.1080.0780.0200.0580.0920.0510.400-0.0340.0680.022
Q4A0.3090.2530.2760.2960.3680.3210.2260.2260.2760.2540.3171.0000.3360.254-0.0710.0500.116-0.0760.348-0.1160.0920.0130.136-0.2780.0840.0610.0120.0810.0630.0500.407-0.0340.0560.030
Q7A0.3140.2830.2880.2880.3080.3220.2300.2450.2860.2630.4340.3361.0000.259-0.0670.0640.109-0.0870.339-0.1210.1000.0180.149-0.2590.1020.0720.0250.0640.0830.0560.419-0.0380.0770.027
Q9A0.2230.2230.3290.2000.2630.3440.2060.3270.3370.3690.2550.2540.2591.000-0.1190.1060.124-0.1060.440-0.1560.1480.0410.163-0.3210.1140.0660.0270.0810.1090.0350.437-0.0780.0810.023
TIPI1-0.073-0.056-0.110-0.057-0.069-0.103-0.052-0.133-0.092-0.133-0.075-0.071-0.067-0.1191.000-0.0720.1330.193-0.0910.312-0.3760.158-0.0230.1920.0460.0330.0620.0590.0460.0320.076-0.0700.1020.036
TIPI100.0520.0720.1150.0720.0390.0700.0840.1410.1320.1360.0610.0500.0640.106-0.0721.0000.054-0.0760.139-0.1880.112-0.0460.203-0.0330.0850.0440.1340.0670.0880.0420.074-0.1690.1860.031
TIPI20.1110.1020.1210.1070.1100.1290.1110.1280.1370.1290.1080.1160.1090.1240.1330.0541.0000.0150.2570.010-0.007-0.0690.130-0.1860.0690.035-0.0040.0150.0610.0230.093-0.0170.0110.033
TIPI3-0.081-0.080-0.132-0.079-0.064-0.113-0.071-0.149-0.146-0.136-0.082-0.076-0.087-0.1060.193-0.0760.0151.000-0.0920.248-0.0020.159-0.3230.2720.0620.0680.0290.0460.0700.0170.0800.0090.0160.031
TIPI40.2960.2850.4460.2720.3300.4460.2480.3930.4400.4510.3220.3480.3390.440-0.0910.1390.257-0.0921.000-0.1690.1920.0630.218-0.4630.0760.052-0.0090.1150.0790.0370.275-0.0240.0460.037
TIPI5-0.100-0.117-0.179-0.107-0.092-0.151-0.078-0.185-0.174-0.183-0.119-0.116-0.121-0.1560.312-0.1880.0100.248-0.1691.000-0.1310.177-0.0550.2590.0520.056-0.0070.0450.0470.0210.1000.021-0.0310.024
TIPI60.1040.0810.1440.0940.1040.1240.0870.1570.1380.1650.1010.0920.1000.148-0.3760.112-0.007-0.0020.192-0.1311.0000.0810.064-0.0440.0280.028-0.0270.0450.0570.0190.0920.022-0.0530.024
TIPI70.0110.0070.018-0.0110.0350.0340.0080.0100.0240.0330.0120.0130.0180.0410.158-0.046-0.0690.1590.0630.1770.0811.000-0.0020.1090.0380.0290.0240.0700.0410.0220.035-0.0120.0280.015
TIPI80.1280.1340.1860.1330.1220.1690.1370.2070.2030.2040.1430.1360.1490.163-0.0230.2030.130-0.3230.218-0.0550.064-0.0021.000-0.1830.0850.0740.0310.0200.0920.0290.122-0.0600.0760.017
TIPI9-0.242-0.210-0.344-0.209-0.254-0.352-0.188-0.308-0.351-0.334-0.254-0.278-0.259-0.3210.192-0.033-0.1860.272-0.4630.259-0.0440.109-0.1831.0000.0650.0610.0560.0710.0650.0390.211-0.0450.0440.030
age_group0.0750.1080.1110.0810.0680.0900.0730.1120.1220.1540.1080.0840.1020.1140.0460.0850.0690.0620.0760.0520.0280.0380.0850.0651.0000.447-0.0010.0830.4170.0470.1220.130-0.1140.062
education0.0460.0770.0630.0490.0460.0630.0410.0620.0620.0870.0780.0610.0720.0660.0330.0440.0350.0680.0520.0560.0280.0290.0740.0610.4471.0000.0440.0350.1770.0390.086-0.0080.0070.033
familysize0.0110.0020.0240.044-0.004-0.0230.0590.0430.0670.0310.0200.0120.0250.0270.0620.134-0.0040.029-0.009-0.007-0.0270.0240.0310.056-0.0010.0441.0000.0800.0490.0460.017-0.2440.3540.093
gender0.0600.0240.1050.0610.0490.0830.0390.0730.1010.0850.0580.0810.0640.0810.0590.0670.0150.0460.1150.0450.0450.0700.0200.0710.0830.0350.0801.0000.0250.1080.089-0.0990.1590.052
married0.0540.0860.1150.0610.0620.0770.0690.1150.1150.1470.0920.0630.0830.1090.0460.0880.0610.0700.0790.0470.0570.0410.0920.0650.4170.1770.0490.0251.0000.0420.1320.149-0.1130.033
orientation0.0520.0470.0510.0550.0490.0510.0310.0380.0440.0400.0510.0500.0560.0350.0320.0420.0230.0170.0370.0210.0190.0220.0290.0390.0470.0390.0460.1080.0421.0000.0500.0290.0240.056
predict0.3590.3380.4540.3320.3990.4630.3210.4070.4580.4430.4000.4070.4190.4370.0760.0740.0930.0800.2750.1000.0920.0350.1220.2110.1220.0860.0170.0890.1320.0501.0000.047-0.0620.030
race-0.021-0.040-0.071-0.049-0.017-0.006-0.089-0.087-0.120-0.097-0.034-0.034-0.038-0.078-0.070-0.169-0.0170.009-0.0240.0210.022-0.012-0.060-0.0450.130-0.008-0.244-0.0990.1490.0290.0471.000-0.3770.077
religion0.0370.0580.0880.0800.0340.0240.1050.1010.1450.1040.0680.0560.0770.0810.1020.1860.0110.0160.046-0.031-0.0530.0280.0760.044-0.1140.0070.3540.159-0.1130.024-0.062-0.3771.0000.079
urban0.0300.0270.0320.0370.0190.0260.0240.0190.0350.0290.0220.0300.0270.0230.0360.0310.0330.0310.0370.0240.0240.0150.0170.0300.0620.0330.0930.0520.0330.0560.0300.0770.0791.000

Missing values

2024-02-19T11:31:45.235161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-19T11:31:46.220475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Q2AQ4AQ7AQ9AQ15AQ19AQ20AQ23AQ25AQ28AQ30AQ36AQ40AQ41ATIPI1TIPI2TIPI3TIPI4TIPI5TIPI6TIPI7TIPI8TIPI9TIPI10educationurbangenderreligionorientationracemarriedfamilysizeage_grouppredict
02032101000203311746461611324570152Severe
113333333333333253655563332210110143Extremely Severe
20222202211211125653263552327260143Severe
30010101010000076453263524222260124Normal
433333333132333145757671423112270142Extremely Severe
51023133023233217575712171122260133Extremely Severe
60000031000211251465576213321160123Moderate
731010001022331525526766242112160154Severe
80000000000000035616532723211160324Normal
90103002002223017571654132227160123Severe
Q2AQ4AQ7AQ9AQ15AQ19AQ20AQ23AQ25AQ28AQ30AQ36AQ40AQ41ATIPI1TIPI2TIPI3TIPI4TIPI5TIPI6TIPI7TIPI8TIPI9TIPI10educationurbangenderreligionorientationracemarriedfamilysizeage_grouppredict
2834111120111211120545655654433210410164Severe
2834210000000001000326246635623112170124Normal
2834332320211202012365676777733210110264Extremely Severe
283440122222022011241777771113224170123Severe
2834532231021133232155736643622210120123Extremely Severe
283460000010000100065676465534124160134Normal
2834733323332222222455746474432110110143Extremely Severe
283480001000000000066756361543227130235Normal
283490122001002011316573535343226160123Moderate
2835000000320110120623563551233210110144Moderate